1. Field of the Invention
This application relates generally to the detection or localization of objects in biomedical image data. More particularly, this application relates to the detection or localization of complex structures, such as three-dimensional organs, in biomedical image data using cross-correlation of gradient fields.
2. Description of the Related Art
Biomedical imaging examinations provide detailed information useful for differentiating, diagnosing, or monitoring the condition, structure, and/or extent of various types of tissue within a patient's body. In general, medical imaging examinations detect and record manners in which tissues respond in the presence of applied signals and/or injected or ingested substances, and generate visual representations indicative of such responses. For example, one procedure involves employing magnetic resonance imaging (MRI) technology to produce detectable signals that can be spatially encoded in image data.
In the field of computer vision, object detection or localization is the task of finding a given object in an image or a video sequence. Object detection is valuable in biomedical imaging, where computer vision systems can automatically localize anatomical regions of interest in image data. The prostate, for example, is an excellent object detection candidate for a computer vision system. Manual delineation of the gland is a challenging task for a clinician due to the complex and irregular shape of the prostate. Further still, it is increasingly desirable to precisely target the gland. Radiation therapy, image-guided biopsy, multi-parametric MRI tissue characterization, and organ region retrieval are examples in which accurate prostate detection can play a critical role in a successful patient outcome.
While numerous semi-automatic prostate detection schemes have been described in the prior art, few fully automated schemes currently exist. One existing solution involves the use of image registration methods to detect and segment the prostate. Two papers employing such methods are described, by way of useful background, by Martin et al., “Automated segmentation of the prostate in 3D MR images using a probabilistic atlas and a spatially constrained deformable model,” Medical Physics, 37:1579, 2010; and by Dowling et al., “Automatic atlas-based segmentation of the prostate: a MICCAI 2009 prostate segmentation challenge entry,” available through the World Wide Web of the Internet at the URL address: http://na-mic.org/Wiki/images/f/f1/Dowling_2009_MICCAIProstate_v2.pdf. While these methods may be accurate, detection and segmentation are achieved at high computational cost and unacceptable processing wait times. Furthermore, these described techniques and methods obtain object registration using intensity-based information, which may be suboptimal when applied to medical images with inherent intensity invariance (e.g., magnetic resonance images).